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Verbalizing Activation Differences with Natural Language Autoencoders
Andrea Blasi Núñez, Gianluca Barmina, Federico Torrielli +3
Activation verbalizers translate the internal representations of large language models into human-readable text. The recently proposed natural language autoencoders (NLAs) learn th…
Selecting The Most Informative Tokens in Natural Language Autoencoders
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez +4
Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly. Which positions should an aud…
PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models
Gianluca Barmina, Federico Torrielli, Sven Harms +8
Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpf…
Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion
Stine Lyngsø Beltoft, William Brach, Federico Torrielli +5
Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding huma…
The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
William Brach, Federico Torrielli, Stine Lyngsø Beltoft +3
Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious safety concerns. With the aim of…
Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech
An activation oracle is a language model trained to convert the activations of another model into natural language. However, it is so far unclear to what extent these verbalization…